| Type: | Package |
| Title: | K-Means with Simultaneous Outlier Detection |
| Version: | 0.2.0 |
| Date: | 2022-04-11 |
| Maintainer: | David Charles Howe <kmodR@edgecondition.com> |
| Description: | An implementation of the 'k-means–' algorithm proposed by Chawla and Gionis, 2013 in their paper, "k-means– : A unified approach to clustering and outlier detection. SIAM International Conference on Data Mining (SDM13)", <doi:10.1137/1.9781611972832.21> and using 'ordering' described by Howe, 2013 in the thesis, Clustering and anomaly detection in tropical cyclones". Useful for creating (potentially) tighter clusters than standard k-means and simultaneously finding outliers inexpensively in multidimensional space. |
| License: | GPL-3 |
| Suggests: | testthat |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.2 |
| NeedsCompilation: | no |
| Packaged: | 2022-05-12 11:04:50 UTC; David |
| Author: | David Charles Howe
 [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2022-05-12 11:40:02 UTC |
K-Means clustering with simultaneous Outlier Detection
Description
An implementation of the 'k-means–' algorithm proposed by Chawla and Gionis, 2013 in their paper, "k-means– : A unified approach to clustering and outlier detection. SIAM International Conference on Data Mining (SDM13)", doi: 10.1137/1.9781611972832.21 and using 'ordering' described by Howe, 2013 in the thesis, "Clustering and anomaly detection in tropical cyclones".
Useful for creating (potentially) tighter clusters than standard k-means and simultaneously finding outliers inexpensively in multidimensional space.
Usage
kmod(
X,
k = 5,
l = 0,
i_max = 100,
conv_method = "delta_C",
conv_error = 0,
allow_empty_c = FALSE
)
Arguments
X |
matrix of numeric data or an object that can be coerced to such a matrix (such as a data frame with numeric columns only). |
k |
the number of clusters (default = 5) |
l |
the number of outliers (default = 0) |
i_max |
the maximum number of iterations permissible (default = 100) |
conv_method |
character: the method used to assess if kmod has converged (default = "delta_C") |
conv_error |
numeric: the tolerance permissible when assessing convergence (default = 0) |
allow_empty_c |
logical: set whether empty clusters are permissible (default = FALSE) |
Value
kmod returns a list comprising the following components
k the number of clusters specified
l the number of outliers specified
C the set of cluster centroids
C_sizes cluster sizes
C_ss the sum of squares for each cluster
L the set of outliers
L_dist_sqr the distance squares for each outlier to C
L_index the index of each outlier in the supplied dataset
XC_dist_sqr_assign the distance square and cluster assignment
of each point in the supplied dataset
within_ss the within cluster sum of squares (excludes outliers)
between_ss the between cluster sum of squares
tot_ss the total sum of squares
iterations the number of iterations taken to converge
Examples
# a 2-dimensional example with 2 clusters and 5 outliers
x <- rbind(matrix(rnorm(100, sd = 0.3), ncol = 2),
matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2))
colnames(x) <- c("x", "y")
(cl <- kmod(x, 2, 5))
# cluster a dataset with 8 clusters and 0 outliers
x <- kmod(x, 8)